






import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


snake_case     					=		NewType("""DataClass""", Any)
snake_case     					=		NewType("""DataClassType""", Any)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  if isinstance(lowercase    ,  lowercase					):
    return v
  if v.lower() in ("yes", "true", "t", "y", "1"):
    return True
  elif v.lower() in ("no", "false", "f", "n", "0"):
    return False
  else:
    raise ArgumentTypeError(
        F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''					)


def 	lowerCamelCase__      (  lowercase					):

  """simple docstring"""






  SCREAMING_SNAKE_CASE							:   Any						    =     {str(lowercase					): choice for choice in choices}
  return lambda lowercase					: str_to_choice.get(lowercase    ,  lowercase					)


def 	lowerCamelCase__      (  *,
    lowercase = None    ,  lowercase = None    ,  lowercase = dataclasses.MISSING    ,  lowercase = dataclasses.MISSING    ,  lowercase = None    ,  **lowercase    ,  ):

  """simple docstring"""






  if metadata is None:
    # Important, don't use as default param in function signature because dict is mutable and shared across function calls
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {}
  if aliases is not None:
    SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     aliases
  if help is not None:
    SCREAMING_SNAKE_CASE							:   Tuple						    =     help

  return dataclasses.field(metadata=lowercase    ,  default=lowercase    ,  default_factory=lowercase    ,  **lowercase					)


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     UpperCamelCase_				:		Iterable[DataClassType]


     def __init__(					self							:						Optional[int]       ,			UpperCAmelCase_							:						Union[DataClassType, Iterable[DataClassType]]       ,			**UpperCAmelCase_							:						Optional[int]   ):
       # To make the default appear when using --help
       if "formatter_class" not in kwargs:
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     ArgumentDefaultsHelpFormatter
       super().__init__(**UpperCAmelCase_   )
       if dataclasses.is_dataclass(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     [dataclass_types]
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     list(UpperCAmelCase_   )
       for dtype in self.dataclass_types:
         self._add_dataclass_arguments(UpperCAmelCase_   )


     @staticmethod
     def 					_A							(					UpperCAmelCase_							:						ArgumentParser       ,			UpperCAmelCase_							:						dataclasses.Field   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     f'''--{field.name}'''
       SCREAMING_SNAKE_CASE							:   Tuple						    =     field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type       ,			UpperCAmelCase_   ):
         raise RuntimeError(
             "Unresolved type detected, which should have been done with the help of "
             "`typing.get_type_hints` method by default"   )

       SCREAMING_SNAKE_CASE							:   List[str]						    =     kwargs.pop("aliases"       ,			[]   )
       if isinstance(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   str						    =     [aliases]

       SCREAMING_SNAKE_CASE							:   Dict						    =     getattr(field.type       ,			"__origin__"       ,			field.type   )
       if origin_type is Union or (hasattr(UpperCAmelCase_       ,			"UnionType"   ) and isinstance(UpperCAmelCase_       ,			types.UnionType   )):
         if str not in field.type.__args__ and (
             len(field.type.__args__   ) != 2 or type(UpperCAmelCase_   ) not in field.type.__args__
         ):
           raise ValueError(
               "Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"
               " the argument parser only supports one type per argument."
               f''' Problem encountered in field \'{field.name}\'.'''   )
         if type(UpperCAmelCase_   ) not in field.type.__args__:
           # filter `str` in Union
           SCREAMING_SNAKE_CASE							:   List[str]						    =     field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
           SCREAMING_SNAKE_CASE							:   Tuple						    =     getattr(field.type       ,			"__origin__"       ,			field.type   )
         elif bool not in field.type.__args__:
           # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
           SCREAMING_SNAKE_CASE							:   Dict						    =     (
               field.type.__args__[0] if isinstance(UpperCAmelCase_       ,			field.type.__args__[1]   ) else field.type.__args__[1]
           )
           SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     getattr(field.type       ,			"__origin__"       ,			field.type   )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       SCREAMING_SNAKE_CASE							:   Dict						    =     {}
       if origin_type is Literal or (isinstance(field.type       ,			UpperCAmelCase_   ) and issubclass(field.type       ,			UpperCAmelCase_   )):
         if origin_type is Literal:
           SCREAMING_SNAKE_CASE							:   Any						    =     field.type.__args__
         else:
           SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [x.value for x in field.type]

         SCREAMING_SNAKE_CASE							:   Tuple						    =     make_choice_type_function(kwargs["choices"]   )

         if field.default is not dataclasses.MISSING:
           SCREAMING_SNAKE_CASE							:   Any						    =     field.default
         else:
           SCREAMING_SNAKE_CASE							:   str						    =     True
       elif field.type is bool or field.type == Optional[bool]:
         # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
         # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
         SCREAMING_SNAKE_CASE							:   List[str]						    =     copy(UpperCAmelCase_   )

         # Hack because type=bool in argparse does not behave as we want.
         SCREAMING_SNAKE_CASE							:   Any						    =     string_to_bool
         if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
           # Default value is False if we have no default when of type bool.
           SCREAMING_SNAKE_CASE							:   str						    =     False if field.default is dataclasses.MISSING else field.default
           # This is the value that will get picked if we don't include --field_name in any way
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     default
           # This tells argparse we accept 0 or 1 value after --field_name
           SCREAMING_SNAKE_CASE							:   Tuple						    =     "?"
           # This is the value that will get picked if we do --field_name (without value)
           SCREAMING_SNAKE_CASE							:   List[Any]						    =     True
       elif isclass(UpperCAmelCase_   ) and issubclass(UpperCAmelCase_       ,			UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   List[str]						    =     field.type.__args__[0]
         SCREAMING_SNAKE_CASE							:   List[str]						    =     "+"
         if field.default_factory is not dataclasses.MISSING:
           SCREAMING_SNAKE_CASE							:   Any						    =     field.default_factory()
         elif field.default is dataclasses.MISSING:
           SCREAMING_SNAKE_CASE							:   Optional[int]						    =     True
       else:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     field.type
         if field.default is not dataclasses.MISSING:
           SCREAMING_SNAKE_CASE							:   str						    =     field.default
         elif field.default_factory is not dataclasses.MISSING:
           SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     field.default_factory()
         else:
           SCREAMING_SNAKE_CASE							:   Tuple						    =     True
       parser.add_argument(UpperCAmelCase_       ,			*UpperCAmelCase_       ,			**UpperCAmelCase_   )

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
         SCREAMING_SNAKE_CASE							:   Tuple						    =     False
         parser.add_argument(f'''--no_{field.name}'''       ,			action="store_false"       ,			dest=field.name       ,			**UpperCAmelCase_   )


     def 					_A							(					self							:						int       ,			UpperCAmelCase_							:						DataClassType   ):
       if hasattr(UpperCAmelCase_       ,			"_argument_group_name"   ):
         SCREAMING_SNAKE_CASE							:   Tuple						    =     self.add_argument_group(dtype._argument_group_name   )
       else:
         SCREAMING_SNAKE_CASE							:   Dict						    =     self

       try:
         SCREAMING_SNAKE_CASE							:   Dict[str, type]						    =     get_type_hints(UpperCAmelCase_   )
       except NameError:
         raise RuntimeError(
             f'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
             "removing line of `from __future__ import annotations` which opts in Postponed "
             "Evaluation of Annotations (PEP 563)"   )
       except TypeError as ex:
         # Remove this block when we drop Python 3.9 support
         if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCAmelCase_   ):
           SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     ".".join(map(UpperCAmelCase_       ,			sys.version_info[:3]   )   )
           raise RuntimeError(
               f'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
               "line of `from __future__ import annotations` which opts in union types as "
               "`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To "
               "support Python versions that lower than 3.10, you need to use "
               "`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of "
               "`X | None`."   ) from ex
         raise

       for field in dataclasses.fields(UpperCAmelCase_   ):
         if not field.init:
           continue
         SCREAMING_SNAKE_CASE							:   Tuple						    =     type_hints[field.name]
         self._parse_dataclass_field(UpperCAmelCase_       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]       ,			UpperCAmelCase_							:						List[str]=None       ,			UpperCAmelCase_							:						Optional[Any]=False       ,			UpperCAmelCase_							:						str=True       ,			UpperCAmelCase_							:						Any=None       ,			UpperCAmelCase_							:						str=None       ,			):

       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv   )):
         SCREAMING_SNAKE_CASE							:   Tuple						    =     []

         if args_filename:
           args_files.append(Path(UpperCAmelCase_   )   )
         elif look_for_args_file and len(sys.argv   ):
           args_files.append(Path(sys.argv[0]   ).with_suffix(".args"   )   )

         # args files specified via command line flag should overwrite default args files so we add them last
         if args_file_flag:
           # Create special parser just to extract the args_file_flag values
           SCREAMING_SNAKE_CASE							:   Dict						    =     ArgumentParser()
           args_file_parser.add_argument(UpperCAmelCase_       ,			type=UpperCAmelCase_       ,			action="append"   )

           # Use only remaining args for further parsing (remove the args_file_flag)
           SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Any						    =     args_file_parser.parse_known_args(args=UpperCAmelCase_   )
           SCREAMING_SNAKE_CASE							:   int						    =     vars(UpperCAmelCase_   ).get(args_file_flag.lstrip("-"   )       ,			UpperCAmelCase_   )

           if cmd_args_file_paths:
             args_files.extend([Path(UpperCAmelCase_   ) for p in cmd_args_file_paths]   )

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     []
         for args_file in args_files:
           if args_file.exists():
             file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
         SCREAMING_SNAKE_CASE							:   Any						    =     file_args + args if args is not None else file_args + sys.argv[1:]
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     self.parse_known_args(args=UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     []
       for dtype in self.dataclass_types:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     {f.name for f in dataclasses.fields(UpperCAmelCase_   ) if f.init}
         SCREAMING_SNAKE_CASE							:   Dict						    =     {k: v for k, v in vars(UpperCAmelCase_   ).items() if k in keys}
         for k in keys:
           delattr(UpperCAmelCase_       ,			UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   int						    =     dtype(**UpperCAmelCase_   )
         outputs.append(UpperCAmelCase_   )
       if len(namespace.__dict__   ) > 0:
         # additional namespace.
         outputs.append(UpperCAmelCase_   )
       if return_remaining_strings:
         return (*outputs, remaining_args)
       else:
         if remaining_args:
           raise ValueError(f'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''   )

         return (*outputs,)


     def 					_A							(					self							:						Optional[Any]       ,			UpperCAmelCase_							:						Dict[str, Any]       ,			UpperCAmelCase_							:						bool = False   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     set(args.keys()   )
       SCREAMING_SNAKE_CASE							:   Tuple						    =     []
       for dtype in self.dataclass_types:
         SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     {f.name for f in dataclasses.fields(UpperCAmelCase_   ) if f.init}
         SCREAMING_SNAKE_CASE							:   Dict						    =     {k: v for k, v in args.items() if k in keys}
         unused_keys.difference_update(inputs.keys()   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     dtype(**UpperCAmelCase_   )
         outputs.append(UpperCAmelCase_   )
       if not allow_extra_keys and unused_keys:
         raise ValueError(f'''Some keys are not used by the HfArgumentParser: {sorted(UpperCAmelCase_   )}'''   )
       return tuple(UpperCAmelCase_   )


     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						bool = False   ):
       with open(Path(UpperCAmelCase_   )       ,			encoding="utf-8"   ) as open_json_file:
         SCREAMING_SNAKE_CASE							:   Dict						    =     json.loads(open_json_file.read()   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     self.parse_dict(UpperCAmelCase_       ,			allow_extra_keys=UpperCAmelCase_   )
       return tuple(UpperCAmelCase_   )






     def 					_A							(					self							:						Optional[int]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						bool = False   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.parse_dict(yaml.safe_load(Path(UpperCAmelCase_   ).read_text()   )       ,			allow_extra_keys=UpperCAmelCase_   )
       return tuple(UpperCAmelCase_   )








from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


lowerCamelCase_    :			List[Any]    =	[
    """openmmlab/upernet-convnext-tiny""",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
lowerCamelCase_    :			int    =	"""UperNetConfig"""

class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Union[int, Tuple[int, int]]    ,						snake_case_			:							Union[int, Tuple[int, int], str] = 0    ,						snake_case_			:							bool = False    ,						snake_case_			:							Union[int, Tuple[int, int]] = 1    ,						):
							super().__init__()
							UpperCamelCase_:				Union[str, Any]						=    nn.Convad(
							    in_channels=snake_case_    ,						out_channels=snake_case_    ,						kernel_size=snake_case_    ,						padding=snake_case_    ,						bias=snake_case_    ,						dilation=snake_case_    ,						)
							UpperCamelCase_:				Any						=    nn.BatchNormad(snake_case_   )
							UpperCamelCase_:				int						=    nn.ReLU()




			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							torch.Tensor   ):
							UpperCamelCase_:				str						=    self.conv(snake_case_   )
							UpperCamelCase_:				str						=    self.batch_norm(snake_case_   )
							UpperCamelCase_:				Any						=    self.activation(snake_case_   )

							return output




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int   ):
							super().__init__()
							UpperCamelCase_:				Any						=    [
							    nn.AdaptiveAvgPoolad(snake_case_   ),
							    UperNetConvModule(snake_case_    ,						snake_case_    ,						kernel_size=1   ),
							]
							for i, layer in enumerate(self.layers   ):
											self.add_module(str(snake_case_   )    ,						snake_case_   )




			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							torch.Tensor   ):
							UpperCamelCase_:				str						=    input
							for layer in self.layers:
											UpperCamelCase_:				int						=    layer(snake_case_   )
							return hidden_state




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							List[Any]    ,						snake_case_			:							Tuple[int, ...]    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							bool   ):
							super().__init__()
							UpperCamelCase_:				Any						=    pool_scales
							UpperCamelCase_:				List[str]						=    align_corners
							UpperCamelCase_:				Dict						=    in_channels
							UpperCamelCase_:				int						=    channels
							UpperCamelCase_:				Union[str, Any]						=    []
							for i, pool_scale in enumerate(snake_case_   ):
											UpperCamelCase_:				Dict						=    UperNetPyramidPoolingBlock(pool_scale=snake_case_    ,						in_channels=snake_case_    ,						channels=snake_case_   )
											self.blocks.append(snake_case_   )
											self.add_module(str(snake_case_   )    ,						snake_case_   )




			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							torch.Tensor   ):
							UpperCamelCase_:				Optional[int]						=    []
							for ppm in self.blocks:
											UpperCamelCase_:				int						=    ppm(snake_case_   )
											UpperCamelCase_:				Optional[int]						=    nn.functional.interpolate(
											    snake_case_    ,						size=x.size()[2:]    ,						mode="""bilinear"""    ,						align_corners=self.align_corners   )
											ppm_outs.append(snake_case_   )
							return ppm_outs




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							int    ,						snake_case_			:							Tuple    ,						snake_case_			:							Any   ):
							super().__init__()

							UpperCamelCase_:				Tuple						=    config
							UpperCamelCase_:				Dict						=    config.pool_scales  # e.g. (1, 2, 3, 6)
							UpperCamelCase_:				Tuple						=    in_channels
							UpperCamelCase_:				Dict						=    config.hidden_size
							UpperCamelCase_:				str						=    False
							UpperCamelCase_:				int						=    nn.Convad(self.channels    ,						config.num_labels    ,						kernel_size=1   )

							# PSP Module
							UpperCamelCase_:				Tuple						=    UperNetPyramidPoolingModule(
							    self.pool_scales    ,						self.in_channels[-1]    ,						self.channels    ,						align_corners=self.align_corners    ,						)
							UpperCamelCase_:				int						=    UperNetConvModule(
							    self.in_channels[-1] + len(self.pool_scales   ) * self.channels    ,						self.channels    ,						kernel_size=3    ,						padding=1    ,						)
							# FPN Module
							UpperCamelCase_:				Union[str, Any]						=    nn.ModuleList()
							UpperCamelCase_:				List[str]						=    nn.ModuleList()
							for in_channels in self.in_channels[:-1]:  # skip the top layer
											UpperCamelCase_:				Union[str, Any]						=    UperNetConvModule(snake_case_    ,						self.channels    ,						kernel_size=1   )
											UpperCamelCase_:				List[str]						=    UperNetConvModule(self.channels    ,						self.channels    ,						kernel_size=3    ,						padding=1   )
											self.lateral_convs.append(snake_case_   )
											self.fpn_convs.append(snake_case_   )

							UpperCamelCase_:				Tuple						=    UperNetConvModule(
							    len(self.in_channels   ) * self.channels    ,						self.channels    ,						kernel_size=3    ,						padding=1    ,						)

			def    lowerCAmelCase__						(							self			:							List[str]   ):
							self.apply(self._init_weights   )

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Union[str, Any]   ):
							if isinstance(snake_case_    ,						nn.Convad   ):
											module.weight.data.normal_(mean=0.0    ,						std=self.config.initializer_range   )
											if module.bias is not None:
															module.bias.data.zero_()

			def    lowerCAmelCase__						(							self			:							List[Any]    ,						snake_case_			:							Optional[int]   ):
							UpperCamelCase_:				Tuple						=    inputs[-1]
							UpperCamelCase_:				str						=    [x]
							psp_outs.extend(self.psp_modules(snake_case_   )   )
							UpperCamelCase_:				Union[str, Any]						=    torch.cat(snake_case_    ,						dim=1   )
							UpperCamelCase_:				int						=    self.bottleneck(snake_case_   )

							return output




			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							torch.Tensor   ):
							# build laterals
							UpperCamelCase_:				Union[str, Any]						=    [lateral_conv(encoder_hidden_states[i]   ) for i, lateral_conv in enumerate(self.lateral_convs   )]

							laterals.append(self.psp_forward(snake_case_   )   )

							# build top-down path
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )
							for i in range(used_backbone_levels - 1    ,						0    ,						-1   ):
											UpperCamelCase_:				Union[str, Any]						=    laterals[i - 1].shape[2:]
											UpperCamelCase_:				Union[str, Any]						=    laterals[i - 1] + nn.functional.interpolate(
											    laterals[i]    ,						size=snake_case_    ,						mode="""bilinear"""    ,						align_corners=self.align_corners   )

							# build outputs
							UpperCamelCase_:				Optional[int]						=    [self.fpn_convs[i](laterals[i]   ) for i in range(used_backbone_levels - 1   )]
							# append psp feature
							fpn_outs.append(laterals[-1]   )

							for i in range(used_backbone_levels - 1    ,						0    ,						-1   ):
											UpperCamelCase_:				Any						=    nn.functional.interpolate(
											    fpn_outs[i]    ,						size=fpn_outs[0].shape[2:]    ,						mode="""bilinear"""    ,						align_corners=self.align_corners   )
							UpperCamelCase_:				Any						=    torch.cat(snake_case_    ,						dim=1   )
							UpperCamelCase_:				Any						=    self.fpn_bottleneck(snake_case_   )
							UpperCamelCase_:				Optional[int]						=    self.classifier(snake_case_   )

							return output




class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							List[str]    ,						snake_case_			:							int = 2    ,						snake_case_			:							int = 3    ,						snake_case_			:							Union[int, Tuple[int, int]] = 1   ):
							super().__init__()

							UpperCamelCase_:				Optional[int]						=    config
							UpperCamelCase_:				List[str]						=    config.auxiliary_in_channels
							UpperCamelCase_:				Tuple						=    config.auxiliary_channels
							UpperCamelCase_:				Dict						=    config.auxiliary_num_convs
							UpperCamelCase_:				Tuple						=    config.auxiliary_concat_input
							UpperCamelCase_:				int						=    in_index

							UpperCamelCase_:				Dict						=    (kernel_size // 2) * dilation
							UpperCamelCase_:				int						=    []
							convs.append(
							    UperNetConvModule(
							        self.in_channels    ,						self.channels    ,						kernel_size=snake_case_    ,						padding=snake_case_    ,						dilation=snake_case_   )   )
							for i in range(self.num_convs - 1   ):
											convs.append(
											    UperNetConvModule(
											        self.channels    ,						self.channels    ,						kernel_size=snake_case_    ,						padding=snake_case_    ,						dilation=snake_case_   )   )
							if self.num_convs == 0:
											UpperCamelCase_:				List[str]						=    nn.Identity()
							else:
											UpperCamelCase_:				List[str]						=    nn.Sequential(*snake_case_   )
							if self.concat_input:
											UpperCamelCase_:				Tuple						=    UperNetConvModule(
											    self.in_channels + self.channels    ,						self.channels    ,						kernel_size=snake_case_    ,						padding=kernel_size // 2   )

							UpperCamelCase_:				str						=    nn.Convad(self.channels    ,						config.num_labels    ,						kernel_size=1   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							self.apply(self._init_weights   )

			def    lowerCAmelCase__						(							self			:							Tuple    ,						snake_case_			:							Union[str, Any]   ):
							if isinstance(snake_case_    ,						nn.Convad   ):
											module.weight.data.normal_(mean=0.0    ,						std=self.config.initializer_range   )
											if module.bias is not None:
															module.bias.data.zero_()




			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							torch.Tensor   ):
							# just take the relevant feature maps
							UpperCamelCase_:				Tuple						=    encoder_hidden_states[self.in_index]
							UpperCamelCase_:				Optional[int]						=    self.convs(snake_case_   )
							if self.concat_input:
											UpperCamelCase_:				Optional[int]						=    self.conv_cat(torch.cat([hidden_states, output]    ,						dim=1   )   )
							UpperCamelCase_:				Optional[Any]						=    self.classifier(snake_case_   )
							return output




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    List[Any]          =			UperNetConfig
			__UpperCamelCase   :    Union[str, Any]          =			"""pixel_values"""
			__UpperCamelCase   :    Dict          =			True

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							str   ):
							if isinstance(snake_case_    ,						snake_case_   ):
											module.backbone.init_weights()
											module.decode_head.init_weights()
											module.auxiliary_head.init_weights()

			def    lowerCAmelCase__						(							self			:							Dict   ):
							self.backbone.init_weights()
							self.decode_head.init_weights()
							self.auxiliary_head.init_weights()




			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Tuple    ,						snake_case_			:							Tuple=False   ):
							if isinstance(snake_case_    ,						snake_case_   ):
											UpperCamelCase_:				Union[str, Any]						=    value




lowerCamelCase_    :			Dict    =	r"""
    Parameters:
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCamelCase_    :			Tuple    =	r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
            `attentions` under returned tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
            returned tensors for more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""

@add_start_docstrings(
    """UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes."""  ,							_A  ,							)
class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			def __init__(							self			:							int    ,						snake_case_			:							Dict   ):
							super().__init__(snake_case_   )

							UpperCamelCase_:				int						=    AutoBackbone.from_config(config.backbone_config   )

							# Semantic segmentation head(s)
							UpperCamelCase_:				Optional[Any]						=    UperNetHead(snake_case_    ,						in_channels=self.backbone.channels   )
							UpperCamelCase_:				List[str]						=    UperNetFCNHead(snake_case_   ) if config.use_auxiliary_head else None

							# Initialize weights and apply final processing
							self.post_init()




			@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format("""batch_size, sequence_length"""   )   )
			@replace_return_docstrings(output_type=snake_case_    ,						config_class=_CONFIG_FOR_DOC   )
			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Optional[torch.Tensor] = None    ,						snake_case_			:							Optional[bool] = None    ,						snake_case_			:							Optional[bool] = None    ,						snake_case_			:							Optional[torch.Tensor] = None    ,						snake_case_			:							Optional[bool] = None    ,						):
							UpperCamelCase_:				Union[str, Any]						=    return_dict if return_dict is not None else self.config.use_return_dict
							UpperCamelCase_:				Union[str, Any]						=    (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							UpperCamelCase_:				int						=    output_attentions if output_attentions is not None else self.config.output_attentions

							UpperCamelCase_:				Dict						=    self.backbone.forward_with_filtered_kwargs(
							    snake_case_    ,						output_hidden_states=snake_case_    ,						output_attentions=snake_case_   )
							UpperCamelCase_:				Any						=    outputs.feature_maps

							UpperCamelCase_:				Dict						=    self.decode_head(snake_case_   )
							UpperCamelCase_:				Optional[Any]						=    nn.functional.interpolate(snake_case_    ,						size=pixel_values.shape[2:]    ,						mode="""bilinear"""    ,						align_corners=snake_case_   )

							UpperCamelCase_:				Dict						=    None
							if self.auxiliary_head is not None:
											UpperCamelCase_:				List[str]						=    self.auxiliary_head(snake_case_   )
											UpperCamelCase_:				Optional[int]						=    nn.functional.interpolate(
											    snake_case_    ,						size=pixel_values.shape[2:]    ,						mode="""bilinear"""    ,						align_corners=snake_case_   )

							UpperCamelCase_:				List[Any]						=    None
							if labels is not None:
											if self.config.num_labels == 1:
															raise ValueError("""The number of labels should be greater than one"""   )
											else:
															# compute weighted loss
															UpperCamelCase_:				List[Any]						=    CrossEntropyLoss(ignore_index=self.config.loss_ignore_index   )
															UpperCamelCase_:				Dict						=    loss_fct(snake_case_    ,						snake_case_   )
															UpperCamelCase_:				Tuple						=    loss_fct(snake_case_    ,						snake_case_   )
															UpperCamelCase_:				List[Any]						=    main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

							if not return_dict:
											if output_hidden_states:
															UpperCamelCase_:				Tuple						=    (logits,) + outputs[1:]
											else:
															UpperCamelCase_:				Union[str, Any]						=    (logits,) + outputs[2:]
											return ((loss,) + output) if loss is not None else output

							return SemanticSegmenterOutput(
							    loss=snake_case_    ,						logits=snake_case_    ,						hidden_states=outputs.hidden_states    ,						attentions=outputs.attentions    ,						)






